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학술대회 Markerless Human Body Pose Estimation from Consumer Depth Cameras for Simulator
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저자
이동진, 박찬규, 지수영, 윤호섭, 김재홍
발행일
201510
출처
International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) 2015, pp.398-403
DOI
https://dx.doi.org/10.1109/URAI.2015.7358884
협약과제
15MC2100, 잠재 역량 진단을 위한 감정특이점 기반 맞춤형 인지센싱 및 플랫폼 기술개발, 윤호섭
초록
In recent years, many studies have shown that horse riding exercises have positive effects on promoting both physical and psychological health. To maximize the effects, the correct posture is essential when riding a horse. Therefore, the purpose of this study is to present an algorithm for estimating a human pose from depth data while riding a horse simulator. This estimated information can be used for analyzing the riders posture. The proposed rider pose estimation algorithm is divided into four steps: (1) head detection, (2) body part segmentation, (3) joint position prediction, and (4) updating the joint positions. Each step is dependent on the previous step being completed successfully. We compared the experiment results between our joint prediction algorithm and ground truth data to show the performance of the proposed methodology.
키워드
Consumer Depth Cameras, Corner Detection, Depth Image, Horse Riding, Human Pose Estimation, Robust Regression
KSP 제안 키워드
Consumer Depth Cameras, Depth Data, Depth image, Experiment results, Ground truth data, Horse Riding, Human body, Human pose estimation, Position prediction, Psychological health, Robust regression